ปัจจัยที่ส่งผลต่อการพัฒนาทักษะการทำงานในโรงแรมของแรงงานในอนาคตในยุคปัญญาประดิษฐ์

Narinsiree Chiangphan

Authors

Narinsiree Chiangphan Thai-Nichi Institute of Technology

Keywords:

Working Skill, Hotel, Future, Artificial Intelligence

Abstract

This research article aims 1. to categorize key skills required for hotel work in the era of artificial intelligence and 2. to analyze the influence of various factors on these essential skills. It is a quantitative study that collected data from 400 third-year students majoring in tourism and hospitality-related fields in the central region. The data were analyzed using Exploratory Factor Analysis (EFA) and Multiple Regression Analysis to determine the impact of the identified variables on the required hotel work skills in the AI era.

The findings revealed six groups of influencing factors on essential hotel work skills in the AI era: 1. digital technology competence and hotel system usage, 2. Learning attitudes and self-development, 3. customer orientation and collaboration, 4. Support from faculty, organizations, and colleagues, 5. fundamental factors and working facilities, and 6. motivation from surrounding individuals and society. Among these, three groups were found to have significant influence: learning attitudes and self-development (β=0.757), digital technology competence and hotel system usage (β=0.135), and fundamental factors and working facilities (β=-0.092). However, customer orientation and collaboration, support from people, organizations, and colleagues, and motivation from surrounding individuals and society did not have statistically significant effects
on hotel work skills in the AI era. This reflects the increasing importance of technological adoption in the field. The research findings can be applied as a guideline for curriculum development or instructional activities in tourism and hospitality programs to align with the necessary skills for working in the AI era. This includes promoting learning attitudes, enhancing digital technology and hotel system skills, and creating a supportive learning environment and resources.

References

Bangkok Post. (2023). AI skills deemed vital for workers, students. Retrieved January 20, 2024, from https://shorturl.asia/jwAxL

Chen, C. et al. (2025). From anxiety to action: exploring the impact of artificial intelligence anxiety and artificial intelligence self-efficacy on motivated learning of undergraduate students. Interactive Learning Environments, 33(4), 3162-3177.

Chi, N. T. K. & Hoang Vu, N. (2023). Investigating the customer trust in artificial intelligence: The role of anthropomorphism, empathy response, and interaction. CAAI Transactions on Intelligence Technology, 8(1), 260-273.

Dweck, C. S. (2014). The mindset of a champion. Stanford, CA.: Psychology at Stanford University.

El Archi, Y. & Benbba, B. (2023). The applications of technology acceptance models in tourism and hospitality research: A systematic literature review. Journal of Environmental Management & Tourism, 14(2), 379-391.

Friedrich, R. E., & Kahn, I. (2016). The Role of College Students in Career Development: Perspectives from University Career Centers. The Career Development Quarterly, 64(2), 138-149.

Kim, J. J. & Han, H. (2022). Hotel service innovation with smart technologies: Exploring consumers’ readiness and behaviors. Sustainability, 14(10), 1-15.

Kruesi, M. A. & Bazelmans, L. (2023). Resources, capabilities and competencies: a review of empirical hospitality and tourism research founded on the resource-based view of the firm. Journal of Hospitality and Tourism Insights, 6(2), 549-574.

Laosen, N. et al. (2024, June). Development of AI Chatbot for Tourism Promotion: A Case Study in Ranong and Chumphon, Thailand. Chiang Mai, Thailand: IEEE.

Li, F. et al. (2024). The technology acceptance model and hospitality and tourism consumers’ intention to use mobile technologies: meta-analysis and structural equation modeling. Cornell Hospitality Quarterly, 65(4), 461-477.

Liao, J. et al. (2025). The past, present, and future of AI in hospitality and tourism: a bibliometric analysis. International Journal of Contemporary Hospitality Management, 37(7), 2287-2305.

Ngotngamwong, R. (2020). Artificial intelligence and its impacts on employability. Human Behavior, Development and Society, 21(2), 51-60.

Pickles, M. et al. (2025). Addressing global labour challenges: An integrative model for sustainable hospitality workplaces, informed by resource-based view theory and the kaleidoscope career model. Retrieved January 20, 2024, from https://shorturl.asia/U1qxm

Rahmatillah, M. R. et al. (2024). Exploring self-determination theory and its consequences in hospitality industry; does generative artificial intelligence matters?. Paris: Atlantis Press.

Schmitt, T. A. (2011) Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29(4), 304-321.

Sotiriadis, M. et al. (2020). Influence of Social Networks on Responsible Behaviour by Smart Tourists. Cham: Springer International Publishing.

Ulker-Demirel, E. & Ciftci, G. (2020). A systematic literature review of the theory of planned behavior in tourism, leisure and hospitality management research. Journal of Hospitality and Tourism Management, 43(1), 209-219.

Vasudevan, H. (2021). Resource-based view theory application on the educational service quality. International Journal of Engineering Applied Sciences and Technology, 6(6), 174-186.

World Economic Forum. (2020). The future of jobs report 2020. World Economic Forum. Retrieved January 20, 2024, from https://shorturl.asia/p3h5b

Yamane, T. (1973). Statistics: an introductory analysis. New York: Harper and Row.

Yoelao, W. et al. (2021). Assessing Competencies, Career Adaptability, and Employability of Senior Students in Hospitality and Tourism Program after the COVID-19. Journal of Business, Innovation and Sustainability (JBIS), 19(3), 20-43.

Zhu, C. Z. G. et al. (2024). Examining the effects of Chat GPT on tourism and hospitality student responses through integrating technology acceptance model. International Journal of Tourism Research, 26(4), 1-9.